{"title":"Methods of Analysis of Amazon Product Reviews and Rating Prediction","authors":"","doi":"10.23977/tracam.2023.030102","DOIUrl":null,"url":null,"abstract":"Online shopping reviews have become an important data source for merchants to make smarter decisions in product development, operations, and marketing. In this paper, we propose a modeling strategy to optimize data analysis and processing of online shopping review data. We address four main problems: identifying commonly used words in positive, negative, and helpful reviews, predicting the products to which the comments refer using semantic analysis, predicting the product rating based on the comments using sentiment analysis, and proposing ways to distinguish human comments from machine-generated ones. Additionally, we provide a recommendation letter to customers on how to read product reviews.","PeriodicalId":484637,"journal":{"name":"Transactions on Computational and Applied Mathematics","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Computational and Applied Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23977/tracam.2023.030102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Online shopping reviews have become an important data source for merchants to make smarter decisions in product development, operations, and marketing. In this paper, we propose a modeling strategy to optimize data analysis and processing of online shopping review data. We address four main problems: identifying commonly used words in positive, negative, and helpful reviews, predicting the products to which the comments refer using semantic analysis, predicting the product rating based on the comments using sentiment analysis, and proposing ways to distinguish human comments from machine-generated ones. Additionally, we provide a recommendation letter to customers on how to read product reviews.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
